Community-Detection Method of Complex Network Based on Node Influence Analysis

Author:

Yao Jiaqi12ORCID,Liu Bin3

Affiliation:

1. School of Computer and Mathematical Sciences, University of Adelaide, Adelaide 5005, Australia

2. Haide College, Ocean University of China, Qingdao 266071, China

3. School of Mathematical Sciences, Ocean University of China, Qingdao 266071, China

Abstract

Community detection can help analyze the structural features and functions of complex networks, and plays important roles in many aspects such as project recommendation and network evolution analysis. Therefore, community detection has always been a hot topic in the field of complex networks. Although various community-detection methods have been proposed, how to improve their accuracy and efficiency is still an ambition pursued by researchers. In view of this, this paper proposes a community-detection method for complex networks based on node influence analysis. First, the influence of nodes is represented as a vector composed by neighborhood degree centrality, betweennes centrality and clustering coefficient. Then, Pareto dominance is used to rank the influence of nodes. After that, the community centers are selected by comprehensively considering the node influence and crowding degree. Finally, the remaining nodes are allocated to different communities using a labeling algorithm. The proposed method in this paper is applied to several actual networks. The comparison results with other methods demonstrate the effectiveness of the proposed method.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Reference41 articles.

1. Oligopolistic competition in power networks: A conjectured supply function approach;Day;IEEE Power Eng. Rev.,2002

2. A reduced model for complex network analysis of public transportation systems;Bona;Phys. A Stat. Mech. Appl.,2021

3. Complex Networks: Statistical Properties, Community Structure, and Evolution;Zhang;Math. Probl. Eng.,2015

4. Community detection in networks: A user guide;Fortunato;Phys. A Stat. Mech. Its Appl.,2016

5. Complex network topology mining and community detection;Cao;Dyn. Contin. Discret. Impuls. Syst.,2021

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3